A New Modeling to Feature Selection Based on the Fuzzy Rough Set Theory in Normal and Optimistic States on Hybrid Information Systems
arXiv:2603.08900v1 Announce Type: new Abstract: Considering the high volume, wide variety, and rapid speed of data generation, investigating feature selection methods for big data presents various applications and advantages. By removing irrelevant and redundant features, feature selection reduces data dimensions,...
Cross-Domain Uncertainty Quantification for Selective Prediction: A Comprehensive Bound Ablation with Transfer-Informed Betting
arXiv:2603.08907v1 Announce Type: new Abstract: We present a comprehensive ablation of nine finite-sample bound families for selective prediction with risk control, combining concentration inequalities (Hoeffding, Empirical Bernstein, Clopper-Pearson, Wasserstein DRO, CVaR) with multiple-testing corrections (union bound, Learn Then Test fixed-sequence)...
Quantifying Memorization and Privacy Risks in Genomic Language Models
arXiv:2603.08913v1 Announce Type: new Abstract: Genomic language models (GLMs) have emerged as powerful tools for learning representations of DNA sequences, enabling advances in variant prediction, regulatory element identification, and cross-task transfer learning. However, as these models are increasingly trained or...
Uncovering a Winning Lottery Ticket with Continuously Relaxed Bernoulli Gates
arXiv:2603.08914v1 Announce Type: new Abstract: Over-parameterized neural networks incur prohibitive memory and computational costs for resource-constrained deployment. The Strong Lottery Ticket (SLT) hypothesis suggests that randomly initialized networks contain sparse subnetworks achieving competitive accuracy without weight training. Existing SLT methods,...
Semantic Level of Detail: Multi-Scale Knowledge Representation via Heat Kernel Diffusion on Hyperbolic Manifolds
arXiv:2603.08965v1 Announce Type: new Abstract: AI memory systems increasingly organize knowledge into graph structures -- knowledge graphs, entity relations, community hierarchies -- yet lack a principled mechanism for continuous resolution control: where do the qualitative boundaries between abstraction levels lie,...
MAcPNN: Mutual Assisted Learning on Data Streams with Temporal Dependence
arXiv:2603.08972v1 Announce Type: new Abstract: Internet of Things (IoT) Analytics often involves applying machine learning (ML) models on data streams. In such scenarios, traditional ML paradigms face obstacles related to continuous learning while dealing with concept drifts, temporal dependence, and...
MAPLE: Elevating Medical Reasoning from Statistical Consensus to Process-Led Alignment
arXiv:2603.08987v1 Announce Type: new Abstract: Recent advances in medical large language models have explored Test-Time Reinforcement Learning (TTRL) to enhance reasoning. However, standard TTRL often relies on majority voting (MV) as a heuristic supervision signal, which can be unreliable in...
An accurate flatness measure to estimate the generalization performance of CNN models
arXiv:2603.09016v1 Announce Type: new Abstract: Flatness measures based on the spectrum or the trace of the Hessian of the loss are widely used as proxies for the generalization ability of deep networks. However, most existing definitions are either tailored to...
When to Retrain after Drift: A Data-Only Test of Post-Drift Data Size Sufficiency
arXiv:2603.09024v1 Announce Type: new Abstract: Sudden concept drift makes previously trained predictors unreliable, yet deciding when to retrain and what post-drift data size is sufficient is rarely addressed. We propose CALIPER - a detector- and model-agnostic, data-only test that estimates...
Two Teachers Better Than One: Hardware-Physics Co-Guided Distributed Scientific Machine Learning
arXiv:2603.09032v1 Announce Type: new Abstract: Scientific machine learning (SciML) is increasingly applied to in-field processing, controlling, and monitoring; however, wide-area sensing, real-time demands, and strict energy and reliability constraints make centralized SciML implementation impractical. Most SciML models assume raw data...
SCALAR: Learning and Composing Skills through LLM Guided Symbolic Planning and Deep RL Grounding
arXiv:2603.09036v1 Announce Type: new Abstract: LM-based agents excel when given high-level action APIs but struggle to ground language into low-level control. Prior work has LLMs generate skills or reward functions for RL, but these one-shot approaches lack feedback to correct...
Sim2Act: Robust Simulation-to-Decision Learning via Adversarial Calibration and Group-Relative Perturbation
arXiv:2603.09053v1 Announce Type: new Abstract: Simulation-to-decision learning enables safe policy training in digital environments without risking real-world deployment, and has become essential in mission-critical domains such as supply chains and industrial systems. However, simulators learned from noisy or biased real-world...
Dynamic Multi-period Experts for Online Time Series Forecasting
arXiv:2603.09062v1 Announce Type: new Abstract: Online Time Series Forecasting (OTSF) requires models to continuously adapt to concept drift. However, existing methods often treat concept drift as a monolithic phenomenon. To address this limitation, we first redefine concept drift by categorizing...
Learning Adaptive LLM Decoding
arXiv:2603.09065v1 Announce Type: new Abstract: Decoding from large language models (LLMs) typically relies on fixed sampling hyperparameters (e.g., temperature, top-p), despite substantial variation in task difficulty and uncertainty across prompts and individual decoding steps. We propose to learn adaptive decoding...
PPO-Based Hybrid Optimization for RIS-Assisted Semantic Vehicular Edge Computing
arXiv:2603.09082v1 Announce Type: new Abstract: To support latency-sensitive Internet of Vehicles (IoV) applications amidst dynamic environments and intermittent links, this paper proposes a Reconfigurable Intelligent Surface (RIS)-aided semantic-aware Vehicle Edge Computing (VEC) framework. This approach integrates RIS to optimize wireless...
Overcoming Valid Action Suppression in Unmasked Policy Gradient Algorithms
arXiv:2603.09090v1 Announce Type: new Abstract: In reinforcement learning environments with state-dependent action validity, action masking consistently outperforms penalty-based handling of invalid actions, yet existing theory only shows that masking preserves the policy gradient theorem. We identify a distinct failure mode...
Probabilistic Hysteresis Factor Prediction for Electric Vehicle Batteries with Graphite Anodes Containing Silicon
arXiv:2603.09103v1 Announce Type: new Abstract: Batteries with silicon-graphite-based anodes, which offer higher energy density and improved charging performance, introduce pronounced voltage hysteresis, making state-of-charge (SoC) estimation particularly challenging. Existing approaches to modeling hysteresis rely on exhaustive high-fidelity tests or focus...
Decoupling Reasoning and Confidence: Resurrecting Calibration in Reinforcement Learning from Verifiable Rewards
arXiv:2603.09117v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) significantly enhances large language models (LLMs) reasoning but severely suffers from calibration degeneration, where models become excessively over-confident in incorrect answers. Previous studies devote to directly incorporating calibration objective...
Causally Sufficient and Necessary Feature Expansion for Class-Incremental Learning
arXiv:2603.09145v1 Announce Type: new Abstract: Current expansion-based methods for Class Incremental Learning (CIL) effectively mitigate catastrophic forgetting by freezing old features. However, such task-specific features learned from the new task may collide with the old features. From a causal perspective,...
Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL
arXiv:2603.09161v1 Announce Type: new Abstract: Learning effective netlist representations is fundamentally constrained by the scarcity of labeled datasets, as real designs are protected by Intellectual Property (IP) and costly to annotate. Existing work therefore focuses on small-scale circuits with clean...
GIAT: A Geologically-Informed Attention Transformer for Lithology Identification
arXiv:2603.09165v1 Announce Type: new Abstract: Accurate lithology identification from well logs is crucial for subsurface resource evaluation. Although Transformer-based models excel at sequence modeling, their "black-box" nature and lack of geological guidance limit their performance and trustworthiness. To overcome these...
The Radio-Frequency Transformer for Signal Separation
arXiv:2603.09201v1 Announce Type: new Abstract: We study a problem of signal separation: estimating a signal of interest (SOI) contaminated by an unknown non-Gaussian background/interference. Given the training data consisting of examples of SOI and interference, we show how to build...
Strategically Robust Multi-Agent Reinforcement Learning with Linear Function Approximation
arXiv:2603.09208v1 Announce Type: new Abstract: Provably efficient and robust equilibrium computation in general-sum Markov games remains a core challenge in multi-agent reinforcement learning. Nash equilibrium is computationally intractable in general and brittle due to equilibrium multiplicity and sensitivity to approximation...
Beyond Test-Time Training: Learning to Reason via Hardware-Efficient Optimal Control
arXiv:2603.09221v1 Announce Type: new Abstract: Associative memory has long underpinned the design of sequential models. Beyond recall, humans reason by projecting future states and selecting goal-directed actions, a capability that modern language models increasingly require but do not natively encode....
Transductive Generalization via Optimal Transport and Its Application to Graph Node Classification
arXiv:2603.09257v1 Announce Type: new Abstract: Many existing transductive bounds rely on classical complexity measures that are computationally intractable and often misaligned with empirical behavior. In this work, we establish new representation-based generalization bounds in a distribution-free transductive setting, where learned...
DendroNN: Dendrocentric Neural Networks for Energy-Efficient Classification of Event-Based Data
arXiv:2603.09274v1 Announce Type: new Abstract: Spatiotemporal information is at the core of diverse sensory processing and computational tasks. Feed-forward spiking neural networks can be used to solve these tasks while offering potential benefits in terms of energy efficiency by computing...
A Gaussian Comparison Theorem for Training Dynamics in Machine Learning
arXiv:2603.09310v1 Announce Type: new Abstract: We study training algorithms with data following a Gaussian mixture model. For a specific family of such algorithms, we present a non-asymptotic result, connecting the evolution of the model to a surrogate dynamical system, which...
Reward-Zero: Language Embedding Driven Implicit Reward Mechanisms for Reinforcement Learning
arXiv:2603.09331v1 Announce Type: new Abstract: We introduce Reward-Zero, a general-purpose implicit reward mechanism that transforms natural-language task descriptions into dense, semantically grounded progress signals for reinforcement learning (RL). Reward-Zero serves as a simple yet sophisticated universal reward function that leverages...
TA-GGAD: Testing-time Adaptive Graph Model for Generalist Graph Anomaly Detection
arXiv:2603.09349v1 Announce Type: new Abstract: A significant number of anomalous nodes in the real world, such as fake news, noncompliant users, malicious transactions, and malicious posts, severely compromises the health of the graph data ecosystem and urgently requires effective identification...
Interactive 3D visualization of surface roughness predictions in additive manufacturing: A data-driven framework
arXiv:2603.09353v1 Announce Type: new Abstract: Surface roughness in Material Extrusion Additive Manufacturing varies across a part and is difficult to anticipate during process planning because it depends on both printing parameters and local surface inclination, which governs the staircase effect....